A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring

Wei, Ziquan; Han, Yifeng; Li, Mengya; Yang, Kun; Yang, Yang; Luo, Yi; Ong, Sim Heng

doi:10.3390/rs9090904

Cited by 66 publications

(24 citation statements)

References 35 publications

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“…However, UAV images do have additional challenges when compared to satellite based platforms. For example, multi-sensor fusion is challenged by poor georegistration of multi-date UAV images that can limit the effectiveness of any subsequent analysis of agricultural dynamics [85]. Additionally, flying height, camera specifications and lack of spectral calibration can influence the final accuracy of resulting image mosaics [86].…”

Section: Discussionmentioning

confidence: 99%

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Csillik

Cherbini²,

Johnson

et al. 2018

Drones

162

105

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Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

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Section: Discussionmentioning

confidence: 99%

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Csillik

Cherbini²,

Johnson

et al. 2018

Drones

162

105

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show abstract

“…This issue can be addressed through the use of ground control points (GCPs) during image acquisition [83,109], the location of which is either fixed or measured with specialized equipment more accurate than the camera integrated one (e.g., differential GNSS). Since the latter could prove quite cumbersome and time-consuming for large-scale applications (especially in rice fields which are flooded), automatic registration methods using a reference image can be applied instead, which is an active research topic with many methods having been proposed recently [110][111][112][113]. The latter can reduce the geolocation errors to approximately 1 m or even less, which is sufficient for precision agriculture applications and variable rate fertilization in particular.…”

Section: Discussionmentioning

confidence: 99%

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

et al. 2019

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The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

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“…When drones are used for detecting the occurrence of pests and diseases, the information about pests and diseases of crops should be standardized and digitized [11]. However, for a drone, due to limited carrying weight and battery capacity in a remote large-scale farm, it faces the problems containing short flight time and frequent battery replacement [12]. Nowadays, these characteristics affect the promotion and application of drones in the modern agricultural field.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Gao

Sun

et al. 2020

Sensors

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With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 • C and 16 • C, and high rainfall decreases the spread of wheat powdery mildew.

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A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring

Cited by 66 publications

References 35 publications

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

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